





"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

class 						lowercase   (   _UpperCAmelCase	,    unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = CanineTokenizer
      _SCREAMING_SNAKE_CASE							      = False





      def        _snake_case     (			self					)						->	Any:
            super().setUp()
            lowerCAmelCase    		=			CanineTokenizer()
            tokenizer.save_pretrained(self.tmpdirname					)





      @cached_property
      def        _snake_case     (			self					)						->	Union[str, Any]:
            return CanineTokenizer.from_pretrained("""google/canine-s"""					)





      def        _snake_case     (			self ,		**lowercase					)						->	CanineTokenizer:
            lowerCAmelCase    		=			self.tokenizer_class.from_pretrained(self.tmpdirname ,		**lowercase					)
            lowerCAmelCase    		=			1_024
            return tokenizer





      @require_torch
      def        _snake_case     (			self					)						->	str:
            lowerCAmelCase    		=			self.canine_tokenizer
            lowerCAmelCase    		=			["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
            # fmt: off
            lowerCAmelCase    		=			[57_344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57_345, 0, 0, 0, 0]
            # fmt: on
            lowerCAmelCase    		=			tokenizer(lowercase ,		padding=lowercase ,		return_tensors="""pt"""					)
            self.assertIsInstance(lowercase ,		lowercase					)

            lowerCAmelCase    		=			list(batch.input_ids.numpy()[0]					)

            self.assertListEqual(lowercase ,		lowercase					)

            self.assertEqual((2, 39) ,		batch.input_ids.shape					)
            self.assertEqual((2, 39) ,		batch.attention_mask.shape					)





      @require_torch
      def        _snake_case     (			self					)						->	Optional[Any]:
            lowerCAmelCase    		=			self.canine_tokenizer
            lowerCAmelCase    		=			["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
            lowerCAmelCase    		=			tokenizer(lowercase ,		padding=lowercase ,		return_tensors="""pt"""					)
            # check if input_ids, attention_mask and token_type_ids are returned
            self.assertIn("""input_ids""" ,		lowercase					)
            self.assertIn("""attention_mask""" ,		lowercase					)
            self.assertIn("""token_type_ids""" ,		lowercase					)





      @require_torch
      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			self.canine_tokenizer
            lowerCAmelCase    		=			[
                """What's the weater?""",
                """It's about 25 degrees.""",
            ]
            lowerCAmelCase    		=			tokenizer(
                text_target=lowercase ,		max_length=32 ,		padding="""max_length""" ,		truncation=lowercase ,		return_tensors="""pt"""					)
            self.assertEqual(32 ,		targets["""input_ids"""].shape[1]					)





      def        _snake_case     (			self					)						->	Tuple:
            # safety check on max_len default value so we are sure the test works
            lowerCAmelCase    		=			self.get_tokenizers()
            for tokenizer in tokenizers:
                  with self.subTest(f'{tokenizer.__class__.__name__}'					):
                        self.assertNotEqual(tokenizer.model_max_length ,		42					)

        # Now let's start the test
            lowerCAmelCase    		=			self.get_tokenizers()
            for tokenizer in tokenizers:
                  with self.subTest(f'{tokenizer.__class__.__name__}'					):
                        # Isolate this from the other tests because we save additional tokens/etc
                        lowerCAmelCase    		=			tempfile.mkdtemp()

                        lowerCAmelCase    		=			""" He is very happy, UNwant\u00E9d,running"""
                        lowerCAmelCase    		=			tokenizer.encode(lowercase ,		add_special_tokens=lowercase					)
                        tokenizer.save_pretrained(lowercase					)

                        lowerCAmelCase    		=			tokenizer.__class__.from_pretrained(lowercase					)
                        lowerCAmelCase    		=			after_tokenizer.encode(lowercase ,		add_special_tokens=lowercase					)
                        self.assertListEqual(lowercase ,		lowercase					)

                        shutil.rmtree(lowercase					)

            lowerCAmelCase    		=			self.get_tokenizers(model_max_length=42					)
            for tokenizer in tokenizers:
                  with self.subTest(f'{tokenizer.__class__.__name__}'					):
                        # Isolate this from the other tests because we save additional tokens/etc
                        lowerCAmelCase    		=			tempfile.mkdtemp()

                        lowerCAmelCase    		=			""" He is very happy, UNwant\u00E9d,running"""

                        lowerCAmelCase    		=			tokenizer.additional_special_tokens

                        # We can add a new special token for Canine as follows:
                        lowerCAmelCase    		=			chr(0XE_007					)
                        additional_special_tokens.append(lowercase					)
                        tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}					)
                        lowerCAmelCase    		=			tokenizer.encode(lowercase ,		add_special_tokens=lowercase					)
                        tokenizer.save_pretrained(lowercase					)

                        lowerCAmelCase    		=			tokenizer.__class__.from_pretrained(lowercase					)
                        lowerCAmelCase    		=			after_tokenizer.encode(lowercase ,		add_special_tokens=lowercase					)
                        self.assertListEqual(lowercase ,		lowercase					)
                        self.assertIn(lowercase ,		after_tokenizer.additional_special_tokens					)
                        self.assertEqual(after_tokenizer.model_max_length ,		42					)

                        lowerCAmelCase    		=			tokenizer.__class__.from_pretrained(lowercase ,		model_max_length=43					)
                        self.assertEqual(tokenizer.model_max_length ,		43					)

                        shutil.rmtree(lowercase					)





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			self.get_tokenizers(do_lower_case=lowercase					)
            for tokenizer in tokenizers:
                  with self.subTest(f'{tokenizer.__class__.__name__}'					):
                        lowerCAmelCase		,						lowerCAmelCase    		=			self.get_clean_sequence(lowercase					)

                        # a special token for Canine can be defined as follows:
                        lowerCAmelCase    		=			0XE_005
                        lowerCAmelCase    		=			chr(lowercase					)

                        tokenizer.add_special_tokens({"""cls_token""": special_token}					)
                        lowerCAmelCase    		=			tokenizer.encode(lowercase ,		add_special_tokens=lowercase					)
                        self.assertEqual(len(lowercase					) ,		1					)

                        lowerCAmelCase    		=			tokenizer.decode(ids + encoded_special_token ,		clean_up_tokenization_spaces=lowercase					)
                        lowerCAmelCase    		=			tokenizer.encode(lowercase ,		add_special_tokens=lowercase					)

                        lowerCAmelCase    		=			tokenizer.encode(lowercase ,		add_special_tokens=lowercase					)
                        lowerCAmelCase    		=			tokenizer.encode(lowercase ,		add_special_tokens=lowercase					)
                        self.assertEqual(lowercase ,		input_encoded + special_token_id					)

                        lowerCAmelCase    		=			tokenizer.decode(lowercase ,		skip_special_tokens=lowercase					)
                        self.assertTrue(special_token not in decoded					)





      def        _snake_case     (			self					)						->	Union[str, Any]:
            lowerCAmelCase    		=			self.get_tokenizers(do_lower_case=lowercase					)
            for tokenizer in tokenizers:
                  with self.subTest(f'{tokenizer.__class__.__name__}'					):
                        lowerCAmelCase    		=			chr(0XE_005					)
                        lowerCAmelCase    		=			chr(0XE_006					)

                        # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                        tokenizer.add_tokens([SPECIAL_TOKEN_1] ,		special_tokens=lowercase					)
                        # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                        # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                        tokenizer.add_special_tokens({"""additional_special_tokens""": [SPECIAL_TOKEN_2]}					)

                        lowerCAmelCase    		=			tokenizer.tokenize(lowercase					)
                        lowerCAmelCase    		=			tokenizer.tokenize(lowercase					)

                        self.assertEqual(len(lowercase					) ,		1					)
                        self.assertEqual(len(lowercase					) ,		1					)
                        self.assertEqual(token_a[0] ,		lowercase					)
                        self.assertEqual(token_a[0] ,		lowercase					)





      @require_tokenizers
      def        _snake_case     (			self					)						->	Union[str, Any]:
            lowerCAmelCase    		=			self.get_tokenizers(do_lower_case=lowercase					)
            for tokenizer in tokenizers:
                  with self.subTest(f'{tokenizer.__class__.__name__}'					):
                        # a special token for Canine can be defined as follows:
                        lowerCAmelCase    		=			0XE_006
                        lowerCAmelCase    		=			chr(lowercase					)

                        lowerCAmelCase    		=			AddedToken(lowercase ,		lstrip=lowercase					)
                        tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]}					)

                        with tempfile.TemporaryDirectory() as tmp_dir_name:
                              tokenizer.save_pretrained(lowercase					)
                              tokenizer.from_pretrained(lowercase					)





      def        _snake_case     (			self					)						->	Dict:
            lowerCAmelCase    		=			[]
            if self.test_slow_tokenizer:
                  tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())					)

            if self.test_rust_tokenizer:
                  tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())					)

            for tokenizer_class, tokenizer_utils in tokenizer_list:
                  with tempfile.TemporaryDirectory() as tmp_dir:
                        tokenizer_utils.save_pretrained(lowercase					)

                        with open(os.path.join(lowercase ,		"""special_tokens_map.json"""					) ,		encoding="""utf-8"""					) as json_file:
                              lowerCAmelCase    		=			json.load(lowercase					)

                        with open(os.path.join(lowercase ,		"""tokenizer_config.json"""					) ,		encoding="""utf-8"""					) as json_file:
                              lowerCAmelCase    		=			json.load(lowercase					)

                        # a special token for Canine can be defined as follows:
                        lowerCAmelCase    		=			0XE_006
                        lowerCAmelCase    		=			chr(lowercase					)

                        lowerCAmelCase    		=			[new_token_a]
                        lowerCAmelCase    		=			[new_token_a]

                        with open(os.path.join(lowercase ,		"""special_tokens_map.json"""					) ,		"""w""" ,		encoding="""utf-8"""					) as outfile:
                              json.dump(lowercase ,		lowercase					)
                        with open(os.path.join(lowercase ,		"""tokenizer_config.json"""					) ,		"""w""" ,		encoding="""utf-8"""					) as outfile:
                              json.dump(lowercase ,		lowercase					)

                        # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                        # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                        # "special_tokens_map.json" files
                        lowerCAmelCase    		=			tokenizer_class.from_pretrained(lowercase ,		extra_ids=0					)
                        self.assertIn(lowercase ,		tokenizer_without_change_in_init.additional_special_tokens					)
                        # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                        self.assertEqual(
                            [new_token_a] ,		tokenizer_without_change_in_init.convert_ids_to_tokens(
                                tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]					)					) ,		)

                        lowerCAmelCase    		=			0XE_007
                        lowerCAmelCase    		=			chr(lowercase					)
                        # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                        lowerCAmelCase    		=			[AddedToken(lowercase ,		lstrip=lowercase					)]
                        lowerCAmelCase    		=			tokenizer_class.from_pretrained(
                            lowercase ,		additional_special_tokens=lowercase ,		extra_ids=0					)

                        self.assertIn(lowercase ,		tokenizer.additional_special_tokens					)
                        # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                        self.assertEqual(
                            [new_token_a] ,		tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]					)					)					)





      @require_tokenizers
      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			self.get_tokenizers(do_lower_case=lowercase					)
            for tokenizer in tokenizers:
                  with self.subTest(f'{tokenizer.__class__.__name__}'					):
                        lowerCAmelCase    		=			"""hello world"""
                        if self.space_between_special_tokens:
                              lowerCAmelCase    		=			"""[CLS] hello world [SEP]"""
                        else:
                              lowerCAmelCase    		=			input
                        lowerCAmelCase    		=			tokenizer.encode(lowercase ,		add_special_tokens=lowercase					)
                        lowerCAmelCase    		=			tokenizer.decode(lowercase ,		spaces_between_special_tokens=self.space_between_special_tokens					)
                        self.assertIn(lowercase ,		[output, output.lower()]					)





      def        _snake_case     (			self					)						->	List[str]:
            lowerCAmelCase    		=			self.get_tokenizers()
            for tokenizer in tokenizers:
                  with self.subTest(f'{tokenizer.__class__.__name__}'					):
                        lowerCAmelCase    		=			[
                            """bos_token""",
                            """eos_token""",
                            """unk_token""",
                            """sep_token""",
                            """pad_token""",
                            """cls_token""",
                            """mask_token""",
                        ]

            lowerCAmelCase    		=			"""a"""
            lowerCAmelCase    		=			ord(lowercase					)

            for attr in attributes_list:
                  setattr(lowercase ,		attr + """_id""" ,		lowercase					)
                  self.assertEqual(getattr(lowercase ,		lowercase					) ,		lowercase					)
                  self.assertEqual(getattr(lowercase ,		attr + """_id"""					) ,		lowercase					)

                  setattr(lowercase ,		attr + """_id""" ,		lowercase					)
                  self.assertEqual(getattr(lowercase ,		lowercase					) ,		lowercase					)
                  self.assertEqual(getattr(lowercase ,		attr + """_id"""					) ,		lowercase					)

            setattr(lowercase ,		"""additional_special_tokens_ids""" ,		[]					)
            self.assertListEqual(getattr(lowercase ,		"""additional_special_tokens"""					) ,		[]					)
            self.assertListEqual(getattr(lowercase ,		"""additional_special_tokens_ids"""					) ,		[]					)

            lowerCAmelCase    		=			0XE_006
            lowerCAmelCase    		=			chr(lowercase					)
            setattr(lowercase ,		"""additional_special_tokens_ids""" ,		[additional_special_token_id]					)
            self.assertListEqual(getattr(lowercase ,		"""additional_special_tokens"""					) ,		[additional_special_token]					)
            self.assertListEqual(getattr(lowercase ,		"""additional_special_tokens_ids"""					) ,		[additional_special_token_id]					)





      def        _snake_case     (			self					)						->	Tuple:
            pass





      def        _snake_case     (			self					)						->	Optional[Any]:
            pass





      def        _snake_case     (			self					)						->	Dict:
            pass





      def        _snake_case     (			self					)						->	str:
            pass





      def        _snake_case     (			self					)						->	Optional[int]:
            pass





      def        _snake_case     (			self					)						->	Optional[int]:
            pass





      def        _snake_case     (			self					)						->	Union[str, Any]:
            pass





      def        _snake_case     (			self					)						->	Optional[Any]:
            pass













"""simple docstring"""





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__   =  logging.getLogger(__name__)

@dataclass
class 						lowercase   :
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 42







@dataclass
class 						lowercase   :
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = None
      _SCREAMING_SNAKE_CASE							      = None







class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'train'
      _SCREAMING_SNAKE_CASE							      = 'dev'
      _SCREAMING_SNAKE_CASE							      = 'test'







class 						lowercase   :





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase					)						->	List[InputExample]:
            raise NotImplementedError





      @staticmethod
      def        _snake_case     (			lowercase					)						->	List[str]:
            raise NotImplementedError





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase=False ,		lowercase="[CLS]" ,		lowercase=1 ,		lowercase="[SEP]" ,		lowercase=False ,		lowercase=False ,		lowercase=0 ,		lowercase=0 ,		lowercase=-100 ,		lowercase=0 ,		lowercase=True ,		)						->	List[InputFeatures]:
            lowerCAmelCase    		=			{label: i for i, label in enumerate(lowercase					)}

            lowerCAmelCase    		=			[]
            for ex_index, example in enumerate(lowercase					):
                  if ex_index % 10_000 == 0:
                        logger.info("""Writing example %d of %d""" ,		lowercase ,		len(lowercase					)					)

                  lowerCAmelCase    		=			[]
                  lowerCAmelCase    		=			[]
                  for word, label in zip(example.words ,		example.labels					):
                        lowerCAmelCase    		=			tokenizer.tokenize(lowercase					)

                        # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                        if len(lowercase					) > 0:
                              tokens.extend(lowercase					)
                              # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                              label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(lowercase					) - 1)					)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                  lowerCAmelCase    		=			tokenizer.num_special_tokens_to_add()
                  if len(lowercase					) > max_seq_length - special_tokens_count:
                        lowerCAmelCase    		=			tokens[: (max_seq_length - special_tokens_count)]
                        lowerCAmelCase    		=			label_ids[: (max_seq_length - special_tokens_count)]

                  # The convention in BERT is:
                  # (a) For sequence pairs:
                  #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
                  #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
                  # (b) For single sequences:
                  #  tokens:   [CLS] the dog is hairy . [SEP]
                  #  type_ids:   0   0   0   0  0     0   0
                  #
                  # Where "type_ids" are used to indicate whether this is the first
                  # sequence or the second sequence. The embedding vectors for `type=0` and
                  # `type=1` were learned during pre-training and are added to the wordpiece
                  # embedding vector (and position vector). This is not *strictly* necessary
                  # since the [SEP] token unambiguously separates the sequences, but it makes
                  # it easier for the model to learn the concept of sequences.
                  #
                  # For classification tasks, the first vector (corresponding to [CLS]) is
                  # used as the "sentence vector". Note that this only makes sense because
                  # the entire model is fine-tuned.
                  tokens += [sep_token]
                  label_ids += [pad_token_label_id]
                  if sep_token_extra:
                        # roberta uses an extra separator b/w pairs of sentences
                        tokens += [sep_token]
                        label_ids += [pad_token_label_id]
                  lowerCAmelCase    		=			[sequence_a_segment_id] * len(lowercase					)

                  if cls_token_at_end:
                        tokens += [cls_token]
                        label_ids += [pad_token_label_id]
                        segment_ids += [cls_token_segment_id]
                  else:
                        lowerCAmelCase    		=			[cls_token] + tokens
                        lowerCAmelCase    		=			[pad_token_label_id] + label_ids
                        lowerCAmelCase    		=			[cls_token_segment_id] + segment_ids

                  lowerCAmelCase    		=			tokenizer.convert_tokens_to_ids(lowercase					)

                  # The mask has 1 for real tokens and 0 for padding tokens. Only real
                  # tokens are attended to.
                  lowerCAmelCase    		=			[1 if mask_padding_with_zero else 0] * len(lowercase					)

                  # Zero-pad up to the sequence length.
                  lowerCAmelCase    		=			max_seq_length - len(lowercase					)
                  if pad_on_left:
                        lowerCAmelCase    		=			([pad_token] * padding_length) + input_ids
                        lowerCAmelCase    		=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                        lowerCAmelCase    		=			([pad_token_segment_id] * padding_length) + segment_ids
                        lowerCAmelCase    		=			([pad_token_label_id] * padding_length) + label_ids
                  else:
                        input_ids += [pad_token] * padding_length
                        input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                        segment_ids += [pad_token_segment_id] * padding_length
                        label_ids += [pad_token_label_id] * padding_length

                  assert len(lowercase					) == max_seq_length
                  assert len(lowercase					) == max_seq_length
                  assert len(lowercase					) == max_seq_length
                  assert len(lowercase					) == max_seq_length

                  if ex_index < 5:
                        logger.info("""*** Example ***"""					)
                        logger.info("""guid: %s""" ,		example.guid					)
                        logger.info("""tokens: %s""" ,		""" """.join([str(lowercase					) for x in tokens]					)					)
                        logger.info("""input_ids: %s""" ,		""" """.join([str(lowercase					) for x in input_ids]					)					)
                        logger.info("""input_mask: %s""" ,		""" """.join([str(lowercase					) for x in input_mask]					)					)
                        logger.info("""segment_ids: %s""" ,		""" """.join([str(lowercase					) for x in segment_ids]					)					)
                        logger.info("""label_ids: %s""" ,		""" """.join([str(lowercase					) for x in label_ids]					)					)

                  if "token_type_ids" not in tokenizer.model_input_names:
                        lowerCAmelCase    		=			None

                  features.append(
                      InputFeatures(
                          input_ids=lowercase ,		attention_mask=lowercase ,		token_type_ids=lowercase ,		label_ids=lowercase					)					)
            return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset

       class 						lowercase   (   _UpperCAmelCase   ):
             _SCREAMING_SNAKE_CASE							      = 42
             _SCREAMING_SNAKE_CASE							      = nn.CrossEntropyLoss().ignore_index





             def __init__(			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase = None ,		lowercase=False ,		lowercase = Split.train ,		)						->	List[str]:
                   # Load data features from cache or dataset file
                   lowerCAmelCase    		=			os.path.join(
                       lowercase ,		"""cached_{}_{}_{}""".format(mode.value ,		tokenizer.__class__.__name__ ,		str(lowercase					)					) ,		)

                   # Make sure only the first process in distributed training processes the dataset,
                   # and the others will use the cache.
                   lowerCAmelCase    		=			cached_features_file + """.lock"""
                   with FileLock(lowercase					):
                         if os.path.exists(lowercase					) and not overwrite_cache:
                               logger.info(f'Loading features from cached file {cached_features_file}'					)
                               lowerCAmelCase    		=			torch.load(lowercase					)
                         else:
                               logger.info(f'Creating features from dataset file at {data_dir}'					)
                               lowerCAmelCase    		=			token_classification_task.read_examples_from_file(lowercase ,		lowercase					)
                               # TODO clean up all this to leverage built-in features of tokenizers
                               lowerCAmelCase    		=			token_classification_task.convert_examples_to_features(
                                   lowercase ,		lowercase ,		lowercase ,		lowercase ,		cls_token_at_end=bool(model_type in ["""xlnet"""]					) ,		cls_token=tokenizer.cls_token ,		cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 ,		sep_token=tokenizer.sep_token ,		sep_token_extra=lowercase ,		pad_on_left=bool(tokenizer.padding_side == """left"""					) ,		pad_token=tokenizer.pad_token_id ,		pad_token_segment_id=tokenizer.pad_token_type_id ,		pad_token_label_id=self.pad_token_label_id ,		)
                               logger.info(f'Saving features into cached file {cached_features_file}'					)
                               torch.save(self.features ,		lowercase					)





             def __len__(			self					)						->	int:
                   return len(self.features					)





             def __getitem__(			self ,		lowercase					)						->	InputFeatures:
                   return self.features[i]


if is_tf_available():
       import tensorflow as tf

       class 						lowercase   :
             _SCREAMING_SNAKE_CASE							      = 42
             _SCREAMING_SNAKE_CASE							      = -100





             def __init__(			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase = None ,		lowercase=False ,		lowercase = Split.train ,		)						->	Any:
                   lowerCAmelCase    		=			token_classification_task.read_examples_from_file(lowercase ,		lowercase					)
                   # TODO clean up all this to leverage built-in features of tokenizers
                   lowerCAmelCase    		=			token_classification_task.convert_examples_to_features(
                       lowercase ,		lowercase ,		lowercase ,		lowercase ,		cls_token_at_end=bool(model_type in ["""xlnet"""]					) ,		cls_token=tokenizer.cls_token ,		cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 ,		sep_token=tokenizer.sep_token ,		sep_token_extra=lowercase ,		pad_on_left=bool(tokenizer.padding_side == """left"""					) ,		pad_token=tokenizer.pad_token_id ,		pad_token_segment_id=tokenizer.pad_token_type_id ,		pad_token_label_id=self.pad_token_label_id ,		)

                   def gen():
                         for ex in self.features:
                               if ex.token_type_ids is None:
                                     yield (
                                         {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                         ex.label_ids,
                                     )
                               else:
                                     yield (
                                         {
                                             "input_ids": ex.input_ids,
                                             "attention_mask": ex.attention_mask,
                                             "token_type_ids": ex.token_type_ids,
                                         },
                                         ex.label_ids,
                                     )

                   if "token_type_ids" not in tokenizer.model_input_names:
                         lowerCAmelCase    		=			tf.data.Dataset.from_generator(
                             lowercase ,		({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa) ,		(
                                 {"""input_ids""": tf.TensorShape([None]					), """attention_mask""": tf.TensorShape([None]					)},
                                 tf.TensorShape([None]					),
                             ) ,		)
                   else:
                         lowerCAmelCase    		=			tf.data.Dataset.from_generator(
                             lowercase ,		({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa) ,		(
                                 {
                                     """input_ids""": tf.TensorShape([None]					),
                                     """attention_mask""": tf.TensorShape([None]					),
                                     """token_type_ids""": tf.TensorShape([None]					),
                                 },
                                 tf.TensorShape([None]					),
                             ) ,		)





             def        _snake_case     (			self					)						->	List[str]:
                   lowerCAmelCase    		=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features					)					)					)

                   return self.dataset





             def __len__(			self					)						->	Optional[int]:
                   return len(self.features					)





             def __getitem__(			self ,		lowercase					)						->	InputFeatures:
                   return self.features[i]






